Hardware acceleration of Maximum-Likelihood angle estimation for automotive MIMO radars

Abstract

Direction of arrival (DOA) estimation is an important array signal processing technique, used by various applications such as radar, sonar or wireless communication. Most of the known DOA algorithms suffer from a significant performance reduction and even fail completely under difficult conditions, like small antenna aperture size, correlated signals or a small number of snapshots. Maximum-Likelihood (ML) methods have been investigated thoroughly and are known to still work even in such difficult scenarios. Though, the major drawback of ML methods is their computational cost, especially in the case of large MIMO (multiple-input multiple-output) configurations. This work presents a novel hardware accelerator architecture, which is able to compute the exact ML estimation in the case of one or two targets. It is shown, that the computational demanding vector product can be implemented with the help of CORDIC units, which help to save a considerable amount of hardware resources. Furthermore, the result of the single target estimator can be reused to efficiently compute the estimates in the two-target case. Finally, the performance of the architecture is evaluated by a FPGA implementation which is able to process more than 20 000 detections from 16 channels with 256 steering vectors in real-time (25 Hz).